Built a production-style data pipeline to monitor API performance and reliability. The system collects request-level metrics, stores historical data, computes latency percentiles and error rates, and detects SLA violations using configurable thresholds.
API Probes (Python) → Relational Storage (SQLite) → Analytics & SLA Detection (SQL)
- Request-level latency tracking
- p95 latency computation
- Error rate monitoring
- SLA-based anomaly detection
- Config-driven API monitoring
- Historical metric storage
- Python
- SQL (SQLite)
- Requests
pip install -r requirements.txt
python collector.py
python metrics.py